Outlier detection for compositional data using robust methods
نویسندگان
چکیده
Outlier detection based on the Mahalanobis distance (MD) requires an appropriate transformation in case of compositional data. For the family of logratio transformations (additive, centered and isometric logratio transformation) it is shown that the MDs based on classical estimates are invariant to these transformations, and that the MDs based on affine equivariant estimators of location and covariance are the same for additive and logratio transformation. Moreover, for 3dimensional compositions the data structure can be visualized by contour lines, and in higher dimension the MDs of closed and opened data give an impression of the multivariate data behavior.
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Multivariate outlier detection with compositional data
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